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authorDeterminant <[email protected]>2015-06-06 11:02:48 +0800
committerDeterminant <[email protected]>2015-06-06 11:02:48 +0800
commit0bb9cd4271f127c311fd9839855def8f9ea91dab (patch)
treec9e9a8535ff5405610f380f50a0ea05f8694fef8 /examples/asr_trainer.lua
parent37af4bed9c3680fdb9db569605f15013e9b6b64d (diff)
add ASR DNN trainer
Diffstat (limited to 'examples/asr_trainer.lua')
-rw-r--r--examples/asr_trainer.lua87
1 files changed, 87 insertions, 0 deletions
diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
new file mode 100644
index 0000000..b43a547
--- /dev/null
+++ b/examples/asr_trainer.lua
@@ -0,0 +1,87 @@
+function build_trainer(ifname)
+ local param_repo = make_param_repo(ifname)
+ local sublayer_repo = make_sublayer_repo(param_repo)
+ local layer_repo = make_layer_repo(sublayer_repo, param_repo)
+ local crit = get_criterion_layer(sublayer_repo)
+ local network = get_network(layer_repo)
+ local iterative_trainer = function (ofname, scp_file, bp)
+ gconf.randomize = bp
+ -- build buffer
+ local buffer = make_buffer(make_reader(scp_file, layer_repo))
+ -- initialize the network
+ network:init(gconf.batch_size)
+ gconf.cnt = 0
+ for data in buffer.get_data, buffer do
+ -- prine stat periodically
+ gconf.cnt = gconf.cnt + 1
+ if gconf.cnt == 1000 then
+ print_stat(crit)
+ gconf.cnt = 0
+ end
+ if gconf.cnt == 100 then break end
+
+ input = {data.main_scp, data.phone_state}
+ output = {}
+ err_input = {}
+ err_output = {input[1]:create()}
+ network:propagate(input, output)
+ if bp then
+ network:back_propagate(err_output, err_input, input, output)
+ network:update(err_input, input, output)
+ end
+ -- collect garbage in-time to save GPU memory
+ collectgarbage("collect")
+ end
+ print_stat(crit)
+ if bp then
+ nerv.info("writing back...")
+ cf = nerv.ChunkFile(ofname, "w")
+ for i, p in ipairs(network:get_params()) do
+ cf:write_chunk(p)
+ end
+ cf:close()
+ end
+ return get_accuracy(crit)
+ end
+ return iterative_trainer
+end
+
+dofile(arg[1])
+start_halving_inc = 0.5
+halving_factor = 0.6
+end_halving_inc = 0.1
+min_iter = 1
+max_iter = 20
+min_halving = 6
+gconf.batch_size = 256
+gconf.buffer_size = 81920
+
+local pf0 = gconf.initialized_param
+local trainer = build_trainer(pf0)
+--local trainer = build_trainer("c3.nerv")
+local accu_best = trainer(nil, gconf.cv_scp, false)
+local do_halving = false
+
+nerv.info("initial cross validation: %.3f", accu_best)
+for i = 1, max_iter do
+ nerv.info("iteration %d with lrate = %.6f", i, gconf.lrate)
+ local accu_tr = trainer(pf0 .. "_iter" .. i .. ".nerv", gconf.tr_scp, true)
+ nerv.info("[TR] training set %d: %.3f", i, accu_tr)
+ local accu_new = trainer(nil, gconf.cv_scp, false)
+ nerv.info("[CV] cross validation %d: %.3f", i, accu_new)
+ -- TODO: revert the weights
+ local accu_diff = accu_new - accu_best
+ if do_halving and accu_diff < end_halving_inc and i > min_iter then
+ break
+ end
+ if accu_diff < start_halving_inc and i >= min_halving then
+ do_halving = true
+ end
+ if do_halving then
+ gconf.lrate = gconf.lrate * halving_factor
+ end
+ if accu_new > accu_best then
+ accu_best = accu_new
+ end
+end
+nerv.Matrix.print_profile()